Lettuce classification using convolutional neural network

Determining the varieties of lettuce through image processing and pattern recognition is apart of precision farming. Automatic classification is becoming vital for precision farmingpractice as it is rapidly sprouting field with the emergence of many applications inagriculture. It is a hassling process to differentiate and identify the lettuce varietiesthrough human capabilities as it is time-consuming and also prone to errors in theidentification process. Hence, there is a need to perform this task assisted by a machinecapability which makes it faster with even greater accuracy. The objective of this researchwork is to design lettuce varieties recognition using Convolutional Neural Network(CNN) in MATLAB with an accuracy of at least 90%. CNN was employed to classifyseven types of most commonly found lettuce. The CNN model was trained with 7000leaves and tested with 1800 leaves for the classification of 7 varieties of lettuce. Theoverall classification accuracy is 97.8%; meanwhile, individual classification accuraciesfor the selected lettuce varieties, i.e. Butterhead, Celtuce Love, Italian, Red Coral, LactucaSativa Lettuce, Red Oakleaf and Salad Grand Rapid are 97%, 99.3%, 98.7%, 96%, 100%,99.3%, and 94%, respectively. The results from this study have proven the higheffectiveness of using a machine learning technique, i.e. CNN, to identify a particularvariety of lettuce.